Effect of advanced driver-assistance system trainings on driver workload, knowledge, and trust. (January 2021)

• Record Type: Journal Article
• Authors: Zahabi, Maryam; Razak, Ashiq Mohammed Abdul; Mehta, Ranjana K.; Manser, Michael
• Is Part Of: Transportation research. Volume 76(2021)
• Page Start: 309
• Page End: 320
• Publication Date: 2021-01
• Subjects: ADAS -- Training -- Older adult -- Cognitive load -- Trust -- Knowledge; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2020.12.003
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Demonstration-based training is more effective for training ADAS to male drivers. Video-based training is more effective for training ADAS to female drivers. ADAS training can improve female older adults' trust and knowledge of automation. Abstract: Older adults are more likely to get severely injured or die in vehicle crashes. Advanced driver-assistance systems (ADAS) can reduce their risk of crashes; however, due to the lack of knowledge and training, usage rate of these systems among older drivers is limited. The objective of this study was to evaluate the impact of two ADAS training approaches (i.e., video-based and demonstration-based training) on older drivers' subjective and objective measures of mental workload, knowledge and trust considering drivers' demographic information. Twenty older adults, balanced by gender, participated in a driving simulation study. Results indicated that the video-based training might be more effective for females in reducing their mental workload while driving, whereas the demonstration-based training could be more beneficial for males. There was no significant difference between the video-based and demonstration-based trainings in terms of drivers' trust and knowledge of automation. The findings suggested that ADAS training protocols can potentially be more effective if they are tailored to specific driver demographics.